Method and apparatus for the classification of an article

ABSTRACT

In a process for the classification of an article such as a banknote described by of a k-dimensional feature vector (AGF) which is prepared by a preliminary processing system (7), a test specimen is either assigned to one of n target classes or classified as a counterfeit. For the n target classes n recognition units (15.1 to 15.n) are used, exactly one of the n target classes being recognisable by one recognition unit (15.j) using a respective feature vector (AGFj) prepared for that class. A recognised target class is transmitted by an output unit (14) to a service system (11). There are assigned to a target class in a learning phase several k-dimensional target vectors which are compared with the feature vector during the classification. The recognition unit (15.j) is advantageously a neural network, one neuron comparing the feature vector (AGFj) with one of the target vectors.

FIELD OF THE INVENTION

The invention relates to a method and apparatus for the classificationof an article, particularly, but not exclusively, a monetary unit suchas a banknote or a coin.

BACKGROUND OF THE INVENTION

Such methods are advantageously used in vending machines, changemachines and the like, where classification is carried out, on the onehand, according to value, for example between one-, two- and five-dollarnotes, and/or, on the other hand, between originals and copies orcounterfeits thereof.

The method of the invention can also be applied quite generally for theclassification of test specimens, for example of images, graphs,documents, stamps or signals.

It is known to process intensity values of electromagnetic radiationreflected by image parts of a test specimen in such a manner that thetest specimen can be compared with a pixel matrix (EP 0 067 898 B1) ofan original, or that differences from an original are expressed andevaluated in the form of an angle between two n-dimensional vectors (DE30 40 963 A1) or as a cross-correlation function (EP 0 084 137 A2).

It is further known that valid value ranges of at least two measurementsof a coin or a banknote describe a rectangle (GB 2 238 152 A) or anellipse (GB 2 254 949 A) and that the coin or the banknote is acceptedif a point formed by at least two measurements lies inside the rectangleor the ellipse.

It is also known (CH 640 433 A5) to compare various measurable physicalvariables of a test specimen with corresponding stored threshold valuessubstantially independently of one another and, after successfulclassification, to correct the threshold values using the measurablevariables of the accepted test specimen.

Various formulations for learning classifiers are furthermore known (H.Niemann: "Klassifikation von Mustern"--Berlin, Heidelberg, Berlin,Tokyo: Springer 1983) in which the class ranges are continuously alteredusing classified patterns and which require a considerable amount ofcalculation during the classification, which, in practical use, may leadto unacceptable response times.

In a classification process, particularly for classification of amonetary unit, differentiation between originals and copies/counterfeitsthereof is especially problematical since, on the one hand, originalsand copies/counterfeits thereof are extremely similar to each other ordiffer only slightly in their features and, on the other hand, only asmall number of different copies/counterfeits of an original isavailable. Indeed, some counterfeits may not be available at all whenthe process is set up. A further problem is that the features of anoriginal, for example the features of all genuine ten-frank notes ofdifferent issues, may show a wide dispersion.

It would be desirable to provide a process for the classification of apattern, with which a pattern can be reliably classified even whenfeatures of one class differ little from the corresponding features ofat least one other class and/or when features of the class are widelydispersed, and to create a device with which the process can be carriedout. It would also be desirable to provide a process which is likely tobe capable of distinguishing between genuine and counterfeit articles ofcurrency, even when the counterfeits are not available when the processis being set up.

SUMMARY OF THE INVENTION

Aspects of the present invention are set out in the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

An illustrative embodiment of the invention is described in detail belowwith reference to the drawings, in which:

FIG. 1 is a block diagram of a device for classifying a pattern;

FIG. 2 shows the principle of a classification system;

FIG. 3 shows a recognition unit of the classification system;

FIG. 4 indicates one way in which the recognition unit may operate; and

FIG. 5 is a diagram of the activity spaces of a recognition unit of theclassification system.

DETAILED DESCRIPTION

In FIG. 1, reference numeral 1 denotes a measuring system substantiallycomprising an inlet 2 and a transport system, not shown, for a testspecimen 3 and a group of sensors 4 with which a pattern of the testspecimen 3 is measured. The measuring system 1 is connected by a featurechannel 5 to a preliminary processing system 7 having at least onepreliminary processing activity 6. A classification system 8 isconnected via an input channel 9 to the preliminary processing system 7and via a first output channel 10a to a service system 11. If necessary,the classification system 8 is also connected to the measuring system 1via a second output channel 10b. The measuring system 1, the preliminaryprocessing system 7, the classification system 8 and the service system11 are, therefore, connected by channels substantially to form a chainwhich is terminated by the service system 11.

If necessary, an initialisation system 12 is connected to theclassification system 8 via an initialisation channel 13.

FIG. 2 shows by means of a data flow diagram the construction inprinciple of the classification system 8 arranged between thepreliminary processing system 7 and the service system 11. In the methodof representation chosen, which is known from the literature (D. J.Hatley, I. A. Pirbhai: Strategies for Real-Time System Specification,Dorset House, NY 1988), a circle denotes an activity, a rectangle aterminator and an arrow a communication channel for the transmission ofdata and/or results, the tip of the arrow pointing substantially in thedirection of flow of the data. Furthermore, an arrangement consisting oftwo activities connected by a communication channel is equivalent to asingle activity that fulfils all the functions of the two activities.

Activities are implemented in the form of an electronic circuit and/orin the form of a process, a part of a program or a routine.

The classification system has an output unit 14 which is connected tothe two output channels 10a and 10b, and a specific number n ofrecognition units are connected to the output unit 14, there being shownin FIG. 2, for the sake of a general and simple representation, onlythree of the n recognition units actually present.

15.1 denotes a first recognition unit, which is connected via a firstinput data channel 16.1 for a first input vector AGF₁ to the preliminaryprocessing system 7 and via a first output data channel 17.1 for a firstsignal K₁. 15.2 further denotes a second recognition unit, which isconnected via a second input data channel 16.2 for a second input vectorAGF₂ to the preliminary processing system 7 and via a second output datachannel 17.2 for a second signal K₂. Finally, 15.n denotes an nthrecognition unit, which is connected via an n-th input data channel 16.nfor an n-th input vector AGF_(n) to the preliminary processing system 7and via an n-th output data channel 17.n for an n-th signal K_(n).

Three dots 18 indicate the other recognition units, not shown, each ofwhich is connected via a further input data channel to the preliminaryprocessing system 7 and via a further output data channel to the outputunit 14, each further input data channel being able to transmit afurther input vector AGF and each output data channel being able totransmit a further signal K.

The input channel 9 (FIG. 1) is represented in FIG. 2 by the n inputdata channels 16.1 to 16.n.

Each of the recognition units 15.1 to 15.n is arranged to determinewhether its input vector AGF represents a particular, respective targetclass, and to provide an output signal K in response thereto.Advantageously, there are defined for an original of one class as manytarget classes (and corresponding recognition units 15) as there arescanning directions available in the measurement of physical features ofthe original in the measuring system 1. If the test specimen is adocument printed on both sides, then, for example, the four scanningorientations "face-up moving forwards", "face-up moving backwards","face-down moving forwards" and "face down moving backwards" could beavailable.

In the classification of a test specimen that is either a ten-franknote, a twenty-frank note or a fifty-frank note with each of the fourpossible scanning directions, twelve different target classes, forexample, are obtained.

The output unit 14 informs the service system 11 and/or the measuringsystem 1 either of the target class of the test specimen 1 ascertainedby the classification system 8 or of the fact that the test specimen 1is a counterfeit. Advantageously, the output unit indicates the targetclass of the test specimen 1 when, and only when, exactly one of the nrecognition units 15.1 to 15.n recognises its target class. Otherwisethe test specimen is indicated to be a counterfeit.

In the classification of the test specimen 1, the n recognition units15.1 to 15.n may operate successively (for example using a singleprocessor executing sequential processing), but advantageously theyoperate concurrently, or at least partly concurrently.

Our Swiss Patent Application No. 00 753/92-4, and corresponding U.S.application Ser. No. 08/013,708, filed 4th Feb. 1993 now U.S. Pat. No.5,503,262, and EP-A-560023, (the contents of which are incorporatedherein by reference) disclose a measuring system and a processing systemfor generating a feature vector from values of measured features of atest specimen. The arrangements disclosed therein for this purpose mayalso be used to advantage in apparatus according to the presentinvention. In particular, referring to the description relating to FIGS.1 and 2 in the above-mentioned cases, the receiving system 1, thepre-processing system 7 and the activities 14 and 17 in theclassification system 8, which are performed on the basis of the datareceived from the pre-processing system and the data stored in the datamemory 23, may also be used in an arrangement according to the presentinvention, although the activities 14 and 18 would in the present casebe performed by the preliminary processing system 7 shown in theaccompanying FIG. 1.

In one specific example, the measuring system 1 may be arranged to scana banknote along N lines, using optical sensors. There may be forexample three lines, two on one face of the banknote and one on thereverse face. Each scan line will contain L individual areas, which arescanned in succession. In each area, there may be for examplemeasurements of M different features (for example the reflectanceintensities of red, green and infra-red radiation, where M=3). The totalnumber of measurements for the banknote would therefore be equal toN×M×L. These measurements are delivered to the preliminary processingsystem 7 along the feature channel 5. The system 7 will then derive, foreach scanning point, a k-dimensional local feature vector. Theindividual components of the vector may represent the parametersdescribed in the earlier applications, or alternatively may represent:

(a) The spectrally normalised intensity of the infra-red radiation (i.e.the reflection intensity of the infra-red radiation divided by the sumof the reflected intensities of the infra-red, green and red radiation).

(b) The spectrally normalised intensity of the red radiation.

(c) The spatially normalised intensity of the infra-red radiation (i.e.the intensity of the reflected infra-red radiation divided by the sum ofthe intensities of the infra-red radiation for all scanned areas of thecurrent track).

(d) The spatially normalised intensity of the red radiation.

(e) The spatially normalised intensity of the green radiation.

Instead of using these values directly, they can if desired betransformed using stored data representing mean values and dispersionfactors for those components. For example, each of the k components maycomprise the difference between the spectrally (or spatially) normalisedintensity value and the average of that value, divided by the dispersionfactor.

This will result in the calculation of a k-dimensional local featurevector LFV_(i),l (where i=1 to N, and l=1 to L) for each scanned area,this vector varying for each target class (because the stored averagevalues and dispersion factors differ depending upon target class).

If desired, each component of each of the k-dimensional vectors can thenbe compared with a stored range (which may differ for each targetclass), and the test specimen may be classified as a counterfeit if one(or a predetermined number) of the components lies outside therespective range. Thus, it is possible to avoid further processingoperations if the first pre-processing operation indicates that the testspecimen produces measurements significantly outside those expected forgenuine items.

A second part of the pre-processing operation involves combining thelocal feature vectors LVF_(i),l for each of the lines into a singlek-dimensional global feature vector GFV_(i). There would thus beproduced N such global feature vectors for each test specimen. Theglobal feature vectors may be formed by summing the individualcomponents of each of the local feature vectors associated with theline. In addition, if desired, a further transformation operation can beperformed, similar to that carried out in the first stage of thepre-processing operation. Thus, each summed component may be adjusted bysubtracting from it a stored average value for this component, anddividing by a dispersion factor. Again, these values may vary dependingupon the target class.

The global feature vectors may also be compared with stored ranges. Inthis case also, this may be achieved by comparing each component of thek-dimensional global feature vector with a respective range. The testspecimen is deemed counterfeit if one, or a predetermined number, ofvector components lies outside the respective range.

The third stage of the pre-processing operation involves combining the Nglobal feature vectors GFV_(i). This is achieved by separately summingthe respective components of the vectors to form a single global surfacefeature vector AGF, having k dimensions. Again, each component may betransformed in a similar manner to the transformations mentioned aboveto take into account stored average and/or dispersion data.

The pre-processing system 7 thus results in a surface feature vector AGFwhich will differ depending upon the target class, assuming thattransformation operations taking into account stored data appropriate tothe target classes are used. Respective surface feature vectors AGF₁ toAGF_(n) are then presented to the respective recognition units 15.1 to15.n, as shown in FIG. 2.

It is to be noted that any one or all of the transformation operationsmentioned above could be omitted. In principle, it would be possible topresent the same feature vector AGF to all of the recognition units 15.1to 15.n, and just use the individual characteristics of the recognitionunits for discriminating between classes. However, the use of one ormore of the transformation operations has the advantage of normalisingand compressing the data. Furthermore, it would be possible to arrangefor stored data of a target class to be updated whenever the output unit14 indicates that the test specimen corresponds to the target class. Theuse of a transformation operation based on this updated data wouldtherefore avoid or mitigate problems due to drift, e.g. in the measuringcomponents.

The dimension k can in principle be freely selected and therefore canadvantageously be adapted to the test specimen 3 and/or the measuringsystem 1 and/or the preliminary processing system 7 and/or theclassification system 8. The dimension k is, in the above example, 5,but may be smaller or greater.

Advantageously, each of the n recognition units 15.1 to 15.n is in theform of one neural network. A preferred arrangement of the recognitionunit 15.1 (FIG. 2) to 15.n, which is shown in FIG. 3, comprises an inputlayer 19, a neuron layer 20 and an output layer 21.

The input layer 19 has a fixed number k of inputs and the neuron layer20 has a pre-determined number m of neurons. The output layer 21advantageously has an output component 22 having one output 23 and minputs.

In FIG. 3, for the sake of a general and simple representation, onlythree of the k inputs actually present and only three of the m neuronsactually present are shown.

20.1 denotes a first neuron, 20.2 a second neuron and 20.m an m-thneuron, whilst a first input of the input layer 19 is designated 19.1, asecond input 19.2 and a k-th input 19.k.

Advantageously, each of the m neurons has k inputs, each input of eachneuron 20.1 to 20.m being connected by a respective input weightingcomponent 24_(ji) to each of the k inputs 19.1 to 19.k of the inputlayer 19; in the reference numeral for the input weighting component24_(ji), the index i refers to the i-th input 19.i of the input layer 19and the index j refers to the j-th neuron 20.j connected to the input19.i by the input weighting component 24_(ji). To give an example ofthis, the second neuron 20.2 is connected at its input side by the inputweighting component 24₂₁ to the first input 19.1 of the input layer 19and further connected by the input weighting component 24_(2k) to thek-th input 19.k of the input layer 19.

Each neuron 20.j of the m neurons 20.1 to 20.m is connected at itsoutput side via a respective output weighting component 25.j to theoutput component 22, the index j in the reference numeral for the outputweighting component referring to the j-th neuron 20.j.

The first three dots 26 indicate the inputs 19.x, not shown in FIG. 3,of the input layer 19, the index x being, in the complete integer range,greater than two and less than k, whilst the second three dots 27represent the neurons 20.y that are not shown, the index y being, in thecomplete integer range, greater than two and less than m.

A target class lies inside the k-dimensional space, it being possible todescribe a single target class in general by a plurality of vectors thatare different from one another. The part of the k-dimensional space thatcan be occupied by a target class is advantageously divided intosections in a preparatory or learning phase of the process, the sectionsadjoining or being separate from one another in the space, and eachsection being determined by a respective target vector W whichadvantageously is k-dimensional.

The target class, therefore, is described in general by a number m ofdifferent prototype or target vectors W_(j), it being possible for thenumber of target vectors W_(j) of different target classes to bedifferent. In the embodiment of FIG. 3, each of the m neurons 20 isassociated with a respective target vector W_(j) of the target class. Atarget vector W_(j) of a target class is defined by the weightingcomponents 24_(ji) connected to the neuron 20, which are advantageouslydetermined by learning, and, if necessary, continuously adapted, in thepreparatory phase. The number m may for example be from 5 to 10.

In operation of each of the recognition units 15.1 to 15.n, it isassumed that X represents the associated one of the input surfacefeatures vector AGF₁ to AGF_(n). In each unit there is determined atarget vector W_(c) that, amongst all the m target vectors W_(j) of thetarget class, has the least value of a distance d from the surfacefeature vector X. The distance d is advantageously the Euclideandistance between the target vector W_(j) and the surface feature vectorX. However, the distance d may be a different variable which candetermine that target vector W_(c) which, of the m target vectors W_(j),is closest to the feature vector X. Another example of an advantageousvariable for the distance d is the absolute distance or the Manhattan(city block) distance between a target vector W_(j) and the featurevector X. The Euclidean distance d between two k-dimensional vectorsW_(j) and X is defined as follows:

    d.sub.j = (W.sub.1 -X.sub.1).sup.2 +(W.sub.2 -X.sub.2).sup.2 + . . . +(W.sub.k -X.sub.k).sup.2 !.sup.1/2                       (G 1).

The process for the classification of the pattern that can be describedby a k-dimensional feature vector X can especially advantageously becarried out concurrently if the classification system 8 has at least oneneural network. Advantageously, the neural network is a so-called LVQ(Learning Vector Quantisation) type according to Kohonen (Teuvo Kohonenet al.: Statistical Pattern Recognition with Neural Networks,Proceedings of 2th Annual IEEE International Conference on NeuralNetworks, volume 1, 1988, pages 61 . . . 68) which has the structureshown in FIG. 3.

With j from 1 . . . m, the values of the input weighting components24._(jl) . . . 24._(jm) of the neuron 20.j are advantageously designedaccording to a target vector W_(j), and are variable. The values of theinput weighting components 24._(jl) . . . 24._(jm) are advantageouslydetermined and, if necessary, adapted in the learning phase. Each neuron20.j determines at least the distance d_(j) by receiving at each inputthe difference between the input weighting component 24_(ji) and acomponent of the input vector X, by summing the squares of thesedifferences, and then taking the square root. The neuron 20.j--for jfrom 1 . . . k--transmits to the output layer the logic value "1" onlywhen it has the minimum distance d_(c).

Advantageously, the output component 22 is an OR-gate and the values ofthe weighting components 25.1 to 25.m are set to one. If one recognitionunit outputs a logic "1", the output component 22 transmits this as anindication of a recognised test specimen, and preferably also transmitsan indication of which recognition unit issued the logic "1", therebyindicating the target class.

A normal LVQ network would be arranged so that the neuron 20.c with theminimum distance would always transmit the logic value "1". In thepresent embodiment, the neuron 20.c with the minimum distance d_(c)additionally tests for two further conditions (G2) and (G3), set outbelow. A logic "1" is transmitted only if all three conditions (G1),(G2) and (G3) are fulfilled; otherwise, the neuron 20.c transmits thevalue logic "0".

Accordingly, the determined target vector W_(c) and the feature vector Xare precisely analysed in further process steps in such a manner that itis certain, with an expected reliability, whether the feature vector Xis to be assigned to the target class.

In a first advantageous process step, the greatest magnitude componentof the surface feature vector X is compared with a limiting parameterq_(max), the parameter q_(max) advantageously being determined in thelearning phase. Using a function maximum(), the following condition istherefore obtained:

    Maximum(|X.sub.1 |, |X.sub.2 |, . . . , |X.sub.k |)≦q.sub.max            (G 2).

In a second advantageous process step, the subtraction W_(c) -X iscarried out component by component for all k components and the amountof the difference of two corresponding components is compared with aspace limiting parameter q_(clmax) . . . q_(ckmax) assigned component bycomponent, the k parameters q_(clmax) . . . q_(ckmax) advantageouslybeing determined in the learning phase. With i from 1 to k, thefollowing condition is therefore obtained:

    |W.sub.ci -X.sub.i |≦q.sub.i with i from 1 . . . k(G3).

The feature vector X is assigned to the target class of the targetvector W_(c) when, and only when, the conditions (G2) and (G3) apply.

FIG. 4 shows by way of example how one neuron 20.j may operate. A firstpart 20'.j receives inputs I₁ from the weighting components 24_(ji),calculates the distance d_(j) and sends this to a controlling unit 30.This compares the distances received from all the neurons, and, for theneuron with the shortest distance, sends a signal to a second part 20".jof the neuron. This has inputs I₂, I₃ receiving values q_(max), q_(i)permitting the part to test for conditions (G2), (G3). If the conditionsare met, a logic "1" is output on output line O.

Each target vector W_(j) --for j from 1 . . . k--of a target classadvantageously lies in a part R_(j) of the k-dimensional space that isbounded by polygons of a Voronoi diagram (J. M. Chassery et al.:"Diagramme de Voronoi applique a la segmentation d'images et a ladetection d'evenements en imagenrie multi-sources, Traitement du Signal,volume 8, No. 3).

An activity space of the neuron 20.j--for j from 1 . . . k--isadvantageously a limited region of the part R_(j) of the space, thelimitation being achieved by conditions (G2) and (G3).

FIG. 5 is a representation of the activity space for a particularrecognition unit. For the purpose of simplification and clarity, it isassumed that the input vector X has two dimensions (i.e. k=2), lying inthe plane of the diagram, and that there are four neurons 20.j. Eachneuron is associated with a target vector W_(j), the target vectorsbeing indicated on the diagram by reference numbers 1, 2, 3 and 4. Thelines V₁ to V₆ represent the boundaries of the Voronoi polygons. Thus,the line V₁ between vectors 1 and 2 is defined by those vectors whichare equidistant from the vectors 1 and 2.

The boundaries B1, B2, B3 and B4 are created by condition (G2), andexclude any vectors which lie substantially outside the area ofinterest. It is noted that condition (G2) could alternatively be testedas a final part of the pre-processing stage, so that a vector X is onlypresented to the classification stage if condition (G2) is met.

Within each Voronoi polygon area, there is a shaded area defined byrectilinear boundaries which are created by condition (G3). Withoutcondition G3, any vector lying in the polygon containing vector 2 wouldactivate the associated neuron. However, because of condition (G3), onlyvectors lying within the shaded area containing vector 2 will activatethe neuron, so that the activation area for that neuron has beenrestricted. By applying different restrictions to the different neurons,it will be seen from FIG. 5 that the overall shape of the activationarea for the complete recognition unit can be complex, and can becontrolled to achieve good acceptance of genuine test specimens and goodrejection of counterfeits.

It will be seen from FIG. 5 that the range limits for one component ofthe vector (e.g. 2×q₃₁, being the limits for the first component ofvectors lying within the activity space of neuron 3) may be of adifferent magnitude from the ranges for other components (e.g. 2×q₄₂,being the range limit for the second component of vectors lying in theactivity space of neuron 4). Generally, there would also be storeddifferent range limits for different neurons, so that the range limit2q₄₂ for the second component of vectors lying in the activity space ofneuron 4 would not necessarily be the same as the range limit 2q₂₂,being the range limit for the second component of vectors lying withinthe activity space for neuron 2. Furthermore it is not essential thatthe boundaries be symmetrically located about the target vectors 1, 2, 3and 4.

As shown in FIG. 5, condition (G3) applies rectilinear limits to theactivity spaces. This results from the fact that each component of thedifference between the input vector X and the target vector W_(c) iscompared separately with a respective limit value. This allows forsimple processing. However, it would alternatively be possible to haveother conditions apply, such as a distance measurement. For example, thedistance measurement d_(c) which is derived when determining the neuron20.c associated with the shortest distance between the target vectorW_(c) and the input vector X may be compared with a range to limit theactivity space for the neuron. The result of this would be that theshaded areas in FIG. 5 would no longer have rectilinear boundaries, butwould instead have elliptical boundaries, possibly intersected by thelines V₁ to V₆.

It will also be appreciated, that the boundaries B1 to B4 also need notbe symmetrically distributed, and there may be different values ofq_(max) for different components of the input vector X.

Because the recognition units 15.1 to 15.n are, in known manner,learning neural networks, the values of their m times k input weightingcomponents 24_(ji) --with j from 1 to m and i from 1 to k--can best bedetermined by teaching in known manner. For example, during the trainingprocess the apparatus can be fed with test specimens of known targetclasses, and known counterfeits. Within each recognition unit, it isdetermined which target vector is closest to the surface feature vectorX. If the recognition unit is associated with the correct target classof the test specimen, then the weighting components associated with thattarget vector are adjusted so as to bring it closer to the featurevector X. If the test specimen is not of the associated target class,the weighting components are adjusted to move the target vector awayfrom the feature vector (X). The weighting components associated withthe other target vectors of that recognition unit are not adjusted. (Inan alternative arrangement, the other weighting components may also beadjusted using an adaptive mechanism to increase the convergence speed.)The amounts by which the weighting components are adjusted can initiallybe large, but can be decreased as the training procedure progresses.This allows a very rapid iterative training process which derives thetarget vectors and hence the discriminant surfaces defined by theboundaries of the Voronoi polygons.

As a result of the training process, it is possible to arrange for thetarget vectors for a particular target class to be relatively closetogether, and to be distant from vectors X which are produced as aresult of testing counterfeit specimens. Nevertheless, there may beother counterfeits, perhaps not used in the training process, whichwould produce vectors within the Voronoi polygon associated with atarget vector, such as shown at P in FIG. 4. However, by applyingconditions (G2) and (G3), limits are placed on the permissible valuesfor the input vector X so it is possible to avoid erroneously acceptingsuch a vector P as a genuine specimen. By using a neural network-typearrangement to perform the classification according to discriminant dataderived in an iterative training process, but then applying one or moreboundary tests to limit the acceptance volume, it becomes much easier toavoid erroneously accepting counterfeits, even when those counterfeitsare not used in the training process.

The limiting parameters q_(j) and q_(jimax) --with j from 1 to m and ifrom 1 to k--can advantageously also be determined for all targetclasses by teaching in known manner. Alternatively, they may beseparately determined in such a manner as to reduce the activity spacesufficiently to minimise the risk of counterfeits being classified asgenuine specimens.

One possibility would be for the learning procedure to record whichneuron 20.c is associated with the shortest distance whenever a testspecimen is recognised during the training session. The ranges q_(i) foreach of the k components of the vector W_(c) can then be calculated tobe the standard deviation (or proportional to the standard deviation) ofthe respective component of the vectors X generated in response to thosetest specimens. Any calculated range can then be adjusted, if necessary,to exclude any vectors X generated in response to other test specimens.

If necessary, the starting values required for teaching are entered bymeans of suitable test specimens 1, or they are transmitted to theclassification system 8 by the initialisation system 12.

As indicated above, the parameters used in the transformations appliedto the feature vectors may be updated each time a specimen has beentested and found to correspond with a target class. Alternatively, oradditionally, the weighting components may be updated also, so that theneural networks 15.1 to 15.n are continuously being re-trained duringoperation of the apparatus. The limiting parameters used in condition(G2) and/or (G3) may also be updated in a similar manner.

Although the above embodiment has been described in the context ofmeasurements of reflected colours, the invention is equally applicableto other types of measurements, for example detection of lines ofmagnetic ink on a banknote, or detection of surface contours on a coin.

The above embodiment stores data (the weighting components 24_(ji))defining the target vectors. Alternatively, it is possible only to storedata defining the discriminant surfaces, i.e. the boundaries of theVoronoi polygons.

I claim:
 1. A method of validating an article of currency by determiningwhether the article belongs to a target class associated with aparticular denomination in a particular orientation, the methodcomprising producing a k-dimensional feature vector (X) describing thearticle, determining from among a plurality of target vectors allassociated with said target class that target vector (W_(c)) which isclosest to the feature vector (X), and designating the article asbelonging to the target class if the components of the feature vector(X) meet a predetermined criterion indicating that the feature vector(X) lies within a predetermined boundary containing the closest targetvector (W_(c)).
 2. A method as claimed in claim 1, wherein thecomponents of the feature vector (X) are determined to meet saidpredetermined criterion if the closest target vector (W_(c)) has apredetermined relationship with the feature vector (X).
 3. A method asclaimed in claim 2, wherein the predetermined relationship is differentdepending on which target vector is closest.
 4. A method as claimed inclaim 1, wherein the components of the feature vector (X) are determinedto meet said predetermined criterion if the individual components eachmeet a respective criterion.
 5. A method as claimed in claim 4, whereinthe components of the feature vector (X) are determined to meet saidpredetermined criterion if the difference between each individualcomponent of target vector (W_(c)) and the corresponding component ofthe feature vector (X) is within a predetermined range.
 6. A method asclaimed in claim 1, wherein each of the target vectors associated with atarget class lies within a respective Voronoi polygon, and wherein saidpredetermined criterion restricts the area of the Voronoi polygon withinwhich a feature vector (X) is deemed to represent an article of thetarget class.
 7. A method as claimed in claim 1, wherein the targetvector (W_(c)) closest to the feature vector (X) is determined bymeasuring the Euclidean distance between the target vector (W_(c)) andthe feature vector (X).
 8. A method as claimed in claim 1, wherein atleast one neural network is used to determine the target vector (W_(c))closest to the feature vector (X).
 9. A method as claimed in claim 8,wherein the neural network is an LVQ network.
 10. A method as claimed inclaim 8, wherein, for every target vector of a target class, thedistance d to the feature vector (X) is calculated using one neuron orneuron-like part.
 11. A method as claimed in claim 1, including the stepof deriving the feature vector (X) using stored statistical datarepresentative of the target class.
 12. A method as claimed in claim 11,including the step of updating the statistical data representative ofthe target class on the basis of measurements of a test specimendetermined to belong to that target class.
 13. A method of validating anarticle of currency, the method comprising:taking measurements of thearticle; deriving a feature vector (X) descriptive of the article fromsaid measurements; determining whether the feature vector (X) lieswithin any one of a plurality of Voronoi polygons associated with atarget class representing a particular denomination in a particularorientation; determining whether the feature vector (X) also lies withinan acceptance boundary restricting the area of said one Voronoi polygon;and providing a signal indicating that the article belongs to saidtarget class if (a) the feature vector (X) has been determined to liewithin said Voronoi polygon, and (b) the feature vector (X) also lieswithin said acceptance boundary.
 14. A method of classifying a testarticle as one of a plurality of acceptable denominations of articles ofcurrency, the method comprising applying pre-processing to measurementsof the article using statistical data relating to said acceptabledenominations so as to derive, for each denomination, a respectivefeature vector (X), the components of each feature vector beingrespectively scaled according to the respective statistical data,applying to each feature vector (X) a statistical classification processwhich employs discriminant surfaces defined by respective sets ofweighting values previously derived using an iterative training processfrom training articles known to be valid or invalid examples of saiddenominations, and modifying the statistical data associated with adenomination and used in the pre-processing step in response toclassifying a test article as that denomination.
 15. A method ofclassifying a test article as one of a plurality of acceptabledenominations of articles of currency, comprising applying a statisticalclassification process which employs discriminant surfaces defined byrespective sets of weighting values previously derived using aniterative training process from training articles known to be valid orinvalid examples of said denominations, the classification process beingarranged to distinguish between said denominations, and further applyingan acceptance boundary test which limits the acceptance volume for eachdenomination so as to exclude forgeries not corresponding to saidtraining articles.
 16. Apparatus for validating an article of currency,the apparatus comprising a measuring system, a preliminary processingsystem and a classification system for the classification of an articledescribed by a k-dimensional feature vector (X) within at least npossible target classes, the preliminary processing system beingresponsive to measurements of physical features of a test item suppliedby the measuring system for deriving the feature vector (X) and beingoperable to provide the feature vector (X) to the classification system,wherein the classification system comprises an output unit and aplurality of LVO neural network recognition units each connected at itsinput side to the preliminary processing system and its output side tothe output unit, each recognition unit being operable to recogniseexactly one class, the output unit being arranged to provide an outputindicative of the determined class in response only to exactly onerecognition unit providing an output indicative of the class. 17.Apparatus for validating an article of currency, the apparatuscomprising a measuring system, a preliminary processing system and aclassification system for the classification of an article that can bedescribed by k-dimensional feature vector (X), the preliminaryprocessing system being responsive to measurements of physical featuresof a test specimen supplied by the measuring system for deriving thek-dimensional feature vector (X) and supplying the feature vector to theclassification system, the classification system comprising arecognition unit for determining whether or not the article belongs to atarget class representing a particular denomination in a particularorientation, the recognition unit being operable to determine which,amongst a plurality of target vectors associated with that target class,is the closest target vector (W_(c)) to the feature vector (X), and todesignate the article as belonging to the target class if the componentsof the feature vector (X) meet a predetermined criterion indicating thatthe feature vector (X) lies within a predetermined boundary containingthe closest target vector (W_(c)).
 18. Apparatus according to claim 17,wherein the recognition unit comprises an input layer, and a neuronlayer connected at its input side to the input layer via input weightingcomponents and at its output side to an output layer, the inputweighting components defining the target vectors for the recognitionunit.
 19. Apparatus as claimed in claim 18, wherein the weightingcomponents each has values which have been determined during a trainingprocess.
 20. Apparatus as claimed in claim 18, including means forvarying the values of weighting components in accordance with measuredphysical features of a test specimen when that test specimen has beenfound to belong to the target class of the recognition unit includingthose weighting components, and when the weighting components define thetarget vector closest to the feature vector (X) for that specimen.
 21. Adevice as claimed in claim 17, wherein the preliminary processing systemis operable to derive different feature vectors (X) for respectivetarget classes on the basis of different sets of statistical data eachassociated with a respective denomination, the respective featurevectors (X) each being applied to a respective recognition unit. 22.Apparatus as claimed in claim 21, including means for modifying thestatistical data related to a denomination in accordance with measuredphysical features of an article which has been tested in response todetermining that the article belongs to the class associated with thatdenomination.
 23. Device comprising a measuring system, a preliminaryprocessing system and a classification system for the classification ofa pattern that can be described by a k-dimensional vector (X),especially a pattern of a banknote or a coin, within at least a numberof possible target classes by means of the values of physical featuressupplied by the recording system, wherein the recording system, thepreliminary processing system and the classification system areconnected in the order in which they are listed substantially to form achain at the output of which a service system is connected,characterised in that(a) the classification system comprises an outputunit and several LVO neural network recognition units each connected atits input side to the preliminary processing system and at its outputside to the output unit, and (b) a determined class of the pattern istransmitted by the output unit to an output channel if that class isrecognised by a single recognition unit and no other recognition unitrecognises a class.